AI prototype works for simple cases but fails on multi-step tasks in production?
Single model call cannot handle the complexity of your workflow?
AI Orchestration Services
A single model call is not an AI system. An AI system is a coordinated set of models, tools, and data sources working together to complete tasks that no single model call can handle alone.
We build AI orchestration layers that coordinate models, manage state, route between specialists, handle failures, and deliver reliable outcomes across complex multi-step workflows.
LangGraph, LangChain, and custom orchestration for multi-step AI workflows
Multi-model pipelines -- routing to the right model for each task
Agent memory, state management, and context window handling
Production monitoring, retry logic, and graceful failure handling
RaftLabs builds AI orchestration systems that coordinate multiple models, tools, and data sources to complete complex multi-step workflows. We use LangGraph for stateful agent orchestration, custom orchestration for simpler pipelines, and model routing to direct tasks to the right model at the right cost. Every production AI orchestration system includes state management, failure handling, retry logic, and monitoring. We build orchestration for AI agents, multi-step document workflows, customer support systems, and complex automation pipelines.
The gap between demo and production is orchestration
A ChatGPT demo that works for simple inputs breaks on real-world complexity: documents that don't fit in context, tasks that require multiple steps, workflows where one model's output is another model's input, and errors that need graceful handling rather than full failure.
Orchestration is the engineering that closes that gap.
What we build
Multi-step document workflows
Document processing pipelines that classify, extract, validate, and route in sequence. A document enters the pipeline; structured data exits into your target system. Each step uses the right model for the task -- a fast classifier, a precise extractor, a reasoning model for edge cases. Results from each step feed the next. Exceptions surface to a human review queue rather than failing silently.
AI agent systems
Stateful agents that plan and execute multi-step tasks using tools. The agent receives a goal, reasons about the steps required, calls the appropriate tools (database queries, API calls, calculations), and adjusts its plan based on tool results. LangGraph state management maintains context across the workflow. Guardrails prevent loops, unsafe tool use, and unrecoverable states.
Multi-model pipelines
Routing different tasks to the most cost-effective model: a fast, cheap model (Claude Haiku, GPT-4o mini) for classification and triage, a powerful model (Claude 3.5 Sonnet, GPT-4o) for complex reasoning, a specialist model for code or structured extraction. Smart routing reduces inference cost by 40--70% compared to sending everything to the most capable model, with minimal accuracy trade-off.
RAG with re-ranking
Retrieval pipelines that retrieve more candidates than they use, then re-rank by relevance before including in the model context. Hybrid retrieval (semantic + keyword) for better coverage. Re-ranking with a cross-encoder or LLM judge for precision. Query expansion and reformulation for queries that retrieve poorly on first attempt. The orchestration layer between your data and your model that makes retrieval accurate enough for production.
Human-in-the-loop workflows
AI workflows with defined human intervention points: low-confidence outputs flagged for review, high-stakes decisions requiring approval, and exception cases routed to specialist queues. The AI completes the high-volume routine work autonomously; humans handle the edge cases the system flags. Audit logging for every AI decision and human review action for regulated use cases.
Production monitoring and observability
Tracing for every orchestration step: input, output, latency, token usage, and cost per step. Dashboards for end-to-end workflow performance, failure rates, and cost per completed workflow. Alerting for latency degradation, error rate spikes, and cost anomalies. Evaluation framework for output quality monitoring over time. The observability layer that makes AI systems manageable in production.
Building a multi-step AI workflow?
Tell us what the workflow needs to accomplish, the tools it needs to use, and the reliability requirements. We will design the orchestration architecture.
Related services
Multi-Agent Systems -- multi-agent coordination and specialised agent teams
RAG Pipeline Development -- retrieval-augmented generation infrastructure
MCP Server Development -- tool connectivity for AI agents
Custom AI Development -- end-to-end AI product development
Generative AI Consulting -- architecture strategy before building
Frequently asked questions
AI orchestration is the coordination layer that manages multiple AI models, tools, and data sources working together in a pipeline or agent workflow. A single LLM call handles a single task. AI orchestration handles: calling a retrieval system before the LLM, routing between models based on task type, managing state across multi-step agent workflows, handling tool use results and errors, and retrying failed steps. Orchestration is what turns a demo into a production AI system.
A simple API call is sufficient when: your task is single-step, inputs fit in the context window, you need one model's output, and failure handling is not critical. AI orchestration is needed when: your workflow requires multiple steps (retrieve, analyse, generate, validate), you need to route between models based on task complexity or cost, your agent uses tools that produce results it needs to reason about, you need to maintain state across a conversation or workflow, or failures in one step need graceful fallback rather than a full error.
LangGraph is an open-source orchestration framework for building stateful AI agent workflows as directed graphs. Each node in the graph is an AI step or tool call; edges define the routing logic. LangGraph handles state management, cycles (when an agent needs to loop or retry), and parallel execution. We use LangGraph for complex agent workflows with many states, conditional branching, and human-in-the-loop requirements. For simpler pipelines, custom orchestration without a framework is often cleaner and more maintainable.
Every orchestration step can fail: API rate limits, model unavailability, tool execution errors, and unexpected model outputs. Production orchestration requires: retry logic with exponential backoff for transient failures, fallback paths when a primary model fails, circuit breakers to stop cascading failures, dead letter queues for failed workflow runs that need human review, and alerting when failure rates exceed thresholds. We design failure handling as part of the orchestration architecture -- not as an afterthought.
Multi-step AI workflows accumulate context that can exceed model context windows. Management strategies: summarisation (compress earlier workflow steps into summaries), selective context (include only the most relevant prior steps based on the current task), external memory (store workflow state in a database rather than the context window), and context chunking (process large inputs in segments). The right strategy depends on your workflow structure and the information dependencies between steps.
A focused orchestration layer for a defined workflow (document processing pipeline, customer support agent, or data extraction workflow) typically runs $25,000--$70,000. Complex multi-agent systems with many tools, branching logic, and high reliability requirements run $70,000--$200,000. Orchestration cost is heavily influenced by the number of integration points, the complexity of failure handling requirements, and the need for human-in-the-loop steps.